Convolutional Autoencoder for Defect Detection in Additive Manufacturing
The core idea behind using machine learning (ML) for defect detection is that it can be used to detect flaws as they are being formed in an AM part. As the part is being made, a near-infrared (NIR) sensor records each layer and creates an image of the entire build layer. These images, usually thousands, can be compiled into a ā3Dā array of the entire part. ML tools, such as a convolutional autoencoder (CAE) can go through these images and highlight potential anomalous regions of your part.
In-Situ Monitoringā